Overview

Brought to you by YData

Dataset statistics

Number of variables15
Number of observations1914116
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory549.0 MiB
Average record size in memory300.8 B

Variable types

Numeric12
DateTime2
Categorical1

Alerts

IBNR is highly overall correlated with long and 1 other fieldsHigh correlation
ID_Base is highly overall correlated with transformed_info_messageHigh correlation
arrival_delay_m is highly overall correlated with prev_arrival_delay_m and 2 other fieldsHigh correlation
long is highly overall correlated with IBNRHigh correlation
max_station_number is highly overall correlated with stop_numberHigh correlation
prev_arrival_delay_m is highly overall correlated with arrival_delay_m and 2 other fieldsHigh correlation
prev_departure_delay_m is highly overall correlated with arrival_delay_m and 2 other fieldsHigh correlation
station_progress is highly overall correlated with stop_numberHigh correlation
stop_number is highly overall correlated with max_station_number and 1 other fieldsHigh correlation
transformed_info_message is highly overall correlated with IBNR and 1 other fieldsHigh correlation
weighted_avg_prev_delay is highly overall correlated with arrival_delay_m and 2 other fieldsHigh correlation
IBNR has 65408 (3.4%) zeros Zeros
arrival_delay_m has 1358742 (71.0%) zeros Zeros
prev_arrival_delay_m has 1452766 (75.9%) zeros Zeros
prev_departure_delay_m has 1402573 (73.3%) zeros Zeros
weighted_avg_prev_delay has 1053037 (55.0%) zeros Zeros

Reproduction

Analysis started2024-12-07 08:56:36.850466
Analysis finished2024-12-07 09:05:05.644353
Duration8 minutes and 28.79 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

ID_Base
Real number (ℝ)

High correlation 

Distinct36195
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.236894 × 1016
Minimum-9.223177 × 1018
Maximum9.2208921 × 1018
Zeros0
Zeros (%)0.0%
Negative961232
Negative (%)50.2%
Memory size14.6 MiB
2024-12-07T10:05:05.781621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-9.223177 × 1018
5-th percentile-8.3353319 × 1018
Q1-4.5890671 × 1018
median-4.5229877 × 1016
Q34.5637501 × 1018
95-th percentile8.3281594 × 1018
Maximum9.2208921 × 1018
Range-2.674985 × 1015
Interquartile range (IQR)9.1528171 × 1018

Descriptive statistics

Standard deviation5.3265411 × 1018
Coefficient of variation (CV)-238.1222
Kurtosis-1.1929362
Mean-2.236894 × 1016
Median Absolute Deviation (MAD)4.5719204 × 1018
Skewness0.0095596013
Sum-1.8524912 × 1018
Variance2.8372041 × 1037
MonotonicityNot monotonic
2024-12-07T10:05:05.934064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-2.094717035 × 1018287
 
< 0.1%
-2.325125148 × 1018285
 
< 0.1%
-2.850414882 × 1018280
 
< 0.1%
8.887303232 × 1018280
 
< 0.1%
-7.17271808 × 1018280
 
< 0.1%
8.939332948 × 1018280
 
< 0.1%
3.654108814 × 1018280
 
< 0.1%
-4.024428317 × 1018279
 
< 0.1%
-9.995230551 × 1017279
 
< 0.1%
1.305051853 × 1018279
 
< 0.1%
Other values (36185) 1911307
99.9%
ValueCountFrequency (%)
-9.223176951 × 10185
 
< 0.1%
-9.222235769 × 101835
 
< 0.1%
-9.221813993 × 1018194
< 0.1%
-9.221103336 × 101863
 
< 0.1%
-9.220755073 × 10184
 
< 0.1%
-9.220659516 × 101830
 
< 0.1%
-9.220172063 × 101820
 
< 0.1%
-9.219634608 × 101818
 
< 0.1%
-9.218627477 × 101823
 
< 0.1%
-9.218606938 × 101810
 
< 0.1%
ValueCountFrequency (%)
9.220892138 × 101815
 
< 0.1%
9.22087069 × 101854
 
< 0.1%
9.219589171 × 101814
 
< 0.1%
9.218406789 × 101842
 
< 0.1%
9.218312429 × 101849
 
< 0.1%
9.217323563 × 1018139
< 0.1%
9.217142214 × 10184
 
< 0.1%
9.216409101 × 101825
 
< 0.1%
9.216019188 × 101816
 
< 0.1%
9.215471812 × 101839
 
< 0.1%

ID_Timestamp
Real number (ℝ)

Distinct10066
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4071087 × 109
Minimum2.4070319 × 109
Maximum2.4071424 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.6 MiB
2024-12-07T10:05:06.060960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.4070319 × 109
5-th percentile2.4070807 × 109
Q12.4070914 × 109
median2.4071109 × 109
Q32.4071223 × 109
95-th percentile2.4071415 × 109
Maximum2.4071424 × 109
Range110496
Interquartile range (IQR)30902

Descriptive statistics

Standard deviation21511.693
Coefficient of variation (CV)8.9367352 × 10-6
Kurtosis-0.039654706
Mean2.4071087 × 109
Median Absolute Deviation (MAD)19400
Skewness-0.34028446
Sum4.6074852 × 1015
Variance4.6275293 × 108
MonotonicityNot monotonic
2024-12-07T10:05:06.180061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2407091633 679
 
< 0.1%
2407081633 676
 
< 0.1%
2407111633 665
 
< 0.1%
2407101633 658
 
< 0.1%
2407090733 647
 
< 0.1%
2407080733 645
 
< 0.1%
2407100733 642
 
< 0.1%
2407091533 642
 
< 0.1%
2407121633 640
 
< 0.1%
2407091733 640
 
< 0.1%
Other values (10056) 1907582
99.7%
ValueCountFrequency (%)
2407031857 3
 
< 0.1%
2407040236 4
 
< 0.1%
2407040245 2
 
< 0.1%
2407040253 2
 
< 0.1%
2407040302 4
 
< 0.1%
2407040312 4
 
< 0.1%
2407040313 21
< 0.1%
2407040314 1
 
< 0.1%
2407040317 45
< 0.1%
2407040319 9
 
< 0.1%
ValueCountFrequency (%)
2407142353 5
 
< 0.1%
2407142352 3
 
< 0.1%
2407142351 19
< 0.1%
2407142350 5
 
< 0.1%
2407142349 1
 
< 0.1%
2407142348 19
< 0.1%
2407142347 2
 
< 0.1%
2407142346 11
< 0.1%
2407142345 7
 
< 0.1%
2407142344 8
< 0.1%

stop_number
Real number (ℝ)

High correlation 

Distinct54
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.020377
Minimum1
Maximum54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.6 MiB
2024-12-07T10:05:06.298825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median9
Q316
95-th percentile25
Maximum54
Range53
Interquartile range (IQR)11

Descriptive statistics

Standard deviation7.3168309
Coefficient of variation (CV)0.66393656
Kurtosis0.16188212
Mean11.020377
Median Absolute Deviation (MAD)5
Skewness0.84101858
Sum21094279
Variance53.536014
MonotonicityNot monotonic
2024-12-07T10:05:06.423592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 137299
 
7.2%
3 131099
 
6.8%
4 127699
 
6.7%
5 123202
 
6.4%
6 119199
 
6.2%
7 112947
 
5.9%
8 104947
 
5.5%
9 98293
 
5.1%
10 94049
 
4.9%
11 85393
 
4.5%
Other values (44) 779989
40.7%
ValueCountFrequency (%)
1 14079
 
0.7%
2 137299
7.2%
3 131099
6.8%
4 127699
6.7%
5 123202
6.4%
6 119199
6.2%
7 112947
5.9%
8 104947
5.5%
9 98293
5.1%
10 94049
4.9%
ValueCountFrequency (%)
54 7
 
< 0.1%
53 7
 
< 0.1%
52 6
 
< 0.1%
51 36
< 0.1%
50 33
< 0.1%
49 48
< 0.1%
48 58
< 0.1%
47 60
< 0.1%
46 67
< 0.1%
45 68
< 0.1%

IBNR
Real number (ℝ)

High correlation  Zeros 

Distinct4011
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7748324.3
Minimum0
Maximum8099506
Zeros65408
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size14.6 MiB
2024-12-07T10:05:06.570385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8000075
Q18001897
median8004230
Q38011723
95-th percentile8089101
Maximum8099506
Range8099506
Interquartile range (IQR)9826

Descriptive statistics

Standard deviation1457840.3
Coefficient of variation (CV)0.18814911
Kurtosis24.269478
Mean7748324.3
Median Absolute Deviation (MAD)3034
Skewness-5.1237119
Sum1.4831192 × 1013
Variance2.1252984 × 1012
MonotonicityNot monotonic
2024-12-07T10:05:06.752032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 65408
 
3.4%
8089028 8461
 
0.4%
8004128 8081
 
0.4%
8098549 7416
 
0.4%
8004135 7384
 
0.4%
8004129 7382
 
0.4%
8004131 7374
 
0.4%
8004132 7355
 
0.4%
8089047 7194
 
0.4%
8004136 6958
 
0.4%
Other values (4001) 1781103
93.1%
ValueCountFrequency (%)
0 65408
3.4%
8000001 481
 
< 0.1%
8000002 1
 
< 0.1%
8000004 334
 
< 0.1%
8000007 300
 
< 0.1%
8000009 443
 
< 0.1%
8000010 363
 
< 0.1%
8000011 568
 
< 0.1%
8000012 419
 
< 0.1%
8000013 957
 
< 0.1%
ValueCountFrequency (%)
8099506 194
 
< 0.1%
8098553 4407
0.2%
8098549 7416
0.4%
8098348 4
 
< 0.1%
8098263 6232
0.3%
8098205 1930
 
0.1%
8098193 461
 
< 0.1%
8098147 2459
 
0.1%
8098105 4854
0.3%
8098096 4426
0.2%

long
Real number (ℝ)

High correlation 

Distinct3113
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.213232
Minimum0.834032
Maximum14.982644
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.6 MiB
2024-12-07T10:05:06.873332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.834032
5-th percentile6.838607
Q18.454498
median9.957668
Q312.29175
95-th percentile13.523916
Maximum14.982644
Range14.148612
Interquartile range (IQR)3.837252

Descriptive statistics

Standard deviation2.3062346
Coefficient of variation (CV)0.2258085
Kurtosis-1.2012031
Mean10.213232
Median Absolute Deviation (MAD)1.734955
Skewness0.090998934
Sum19549311
Variance5.3187181
MonotonicityNot monotonic
2024-12-07T10:05:06.996103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.536537 8022
 
0.4%
11.575386 7373
 
0.4%
11.583234 7368
 
0.4%
11.548572 7363
 
0.4%
11.565619 7329
 
0.4%
13.283966 7075
 
0.4%
11.593049 6923
 
0.4%
11.519245 6512
 
0.3%
11.503669 6498
 
0.3%
11.604971 6132
 
0.3%
Other values (3103) 1843521
96.3%
ValueCountFrequency (%)
0.834032 259
 
< 0.1%
0.896632 260
 
< 0.1%
6.070715 1427
0.1%
6.07384 894
< 0.1%
6.074485 1049
0.1%
6.08378 262
 
< 0.1%
6.091499 441
 
< 0.1%
6.094486 1279
0.1%
6.097265 807
< 0.1%
6.098877 286
 
< 0.1%
ValueCountFrequency (%)
14.982644 271
< 0.1%
14.97908 189
< 0.1%
14.930408 259
< 0.1%
14.902088 248
< 0.1%
14.889318 278
< 0.1%
14.825531 304
< 0.1%
14.825234 267
< 0.1%
14.805774 41
 
< 0.1%
14.706775 259
< 0.1%
14.703529 276
< 0.1%

lat
Real number (ℝ)

Distinct3118
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.936138
Minimum47.417954
Maximum55.021381
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.6 MiB
2024-12-07T10:05:07.115746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum47.417954
5-th percentile48.105303
Q149.379389
median51.098294
Q352.496254
95-th percentile53.59793
Maximum55.021381
Range7.6034266
Interquartile range (IQR)3.116865

Descriptive statistics

Standard deviation1.8569028
Coefficient of variation (CV)0.036455509
Kurtosis-1.0465715
Mean50.936138
Median Absolute Deviation (MAD)1.418212
Skewness-0.071097754
Sum97497676
Variance3.4480882
MonotonicityNot monotonic
2024-12-07T10:05:07.237391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48.142623 8022
 
0.4%
48.137048 7373
 
0.4%
48.134202 7368
 
0.4%
48.141969 7363
 
0.4%
48.139452 7329
 
0.4%
52.500737 7075
 
0.4%
48.129168 6923
 
0.4%
48.14354 6512
 
0.3%
48.144371 6498
 
0.3%
48.12744 6132
 
0.3%
Other values (3108) 1843521
96.3%
ValueCountFrequency (%)
47.4179544 259
< 0.1%
47.456591 213
 
< 0.1%
47.5058367 567
< 0.1%
47.513241 428
< 0.1%
47.5251713 290
< 0.1%
47.543785 260
< 0.1%
47.544341 49
 
< 0.1%
47.547219 258
< 0.1%
47.54792 285
< 0.1%
47.549143 264
< 0.1%
ValueCountFrequency (%)
55.021381 286
< 0.1%
55.019862 281
< 0.1%
55.017947 264
< 0.1%
55.01765 273
< 0.1%
55.0149 249
< 0.1%
55.012455 290
< 0.1%
55.010432 309
< 0.1%
55.008077 259
< 0.1%
55.001937 260
< 0.1%
54.988543 303
< 0.1%
Distinct10087
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size14.6 MiB
Minimum2024-07-07 23:32:00
Maximum2024-07-14 23:58:00
2024-12-07T10:05:07.449885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:05:07.615898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct10091
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size14.6 MiB
Minimum2024-07-07 23:32:00
Maximum2024-07-14 23:58:00
2024-12-07T10:05:07.756347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:05:07.903451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

arrival_delay_m
Real number (ℝ)

High correlation  Zeros 

Distinct110
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0484067
Minimum0
Maximum159
Zeros1358742
Zeros (%)71.0%
Negative0
Negative (%)0.0%
Memory size14.6 MiB
2024-12-07T10:05:08.044192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile5
Maximum159
Range159
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.1801873
Coefficient of variation (CV)3.0333528
Kurtosis116.05381
Mean1.0484067
Median Absolute Deviation (MAD)0
Skewness8.0092521
Sum2006772
Variance10.113591
MonotonicityNot monotonic
2024-12-07T10:05:08.219413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1358742
71.0%
1 218086
 
11.4%
2 112971
 
5.9%
3 69359
 
3.6%
4 38702
 
2.0%
5 26169
 
1.4%
6 18020
 
0.9%
7 12762
 
0.7%
8 10065
 
0.5%
9 8115
 
0.4%
Other values (100) 41125
 
2.1%
ValueCountFrequency (%)
0 1358742
71.0%
1 218086
 
11.4%
2 112971
 
5.9%
3 69359
 
3.6%
4 38702
 
2.0%
5 26169
 
1.4%
6 18020
 
0.9%
7 12762
 
0.7%
8 10065
 
0.5%
9 8115
 
0.4%
ValueCountFrequency (%)
159 1
 
< 0.1%
157 1
 
< 0.1%
140 1
 
< 0.1%
136 1
 
< 0.1%
134 1
 
< 0.1%
133 2
 
< 0.1%
120 1
 
< 0.1%
117 1
 
< 0.1%
116 1
 
< 0.1%
110 7
< 0.1%

transformed_info_message
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size126.4 MiB
No message
1379619 
Information
266395 
Bauarbeiten
140053 
Störung
 
121627
Großstörung
 
6422

Length

Max length11
Median length10
Mean length10.025071
Min length7

Characters and Unicode

Total characters19189149
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo message
2nd rowNo message
3rd rowNo message
4th rowNo message
5th rowNo message

Common Values

ValueCountFrequency (%)
No message 1379619
72.1%
Information 266395
 
13.9%
Bauarbeiten 140053
 
7.3%
Störung 121627
 
6.4%
Großstörung 6422
 
0.3%

Length

2024-12-07T10:05:08.412206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-07T10:05:08.617272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
no 1379619
41.9%
message 1379619
41.9%
information 266395
 
8.1%
bauarbeiten 140053
 
4.3%
störung 121627
 
3.7%
großstörung 6422
 
0.2%

Most occurring characters

ValueCountFrequency (%)
e 3039344
15.8%
s 2765660
14.4%
a 1926120
10.0%
o 1918831
10.0%
m 1646014
8.6%
g 1507668
7.9%
N 1379619
7.2%
1379619
7.2%
n 800892
 
4.2%
r 540919
 
2.8%
Other values (11) 2284463
11.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19189149
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 3039344
15.8%
s 2765660
14.4%
a 1926120
10.0%
o 1918831
10.0%
m 1646014
8.6%
g 1507668
7.9%
N 1379619
7.2%
1379619
7.2%
n 800892
 
4.2%
r 540919
 
2.8%
Other values (11) 2284463
11.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19189149
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 3039344
15.8%
s 2765660
14.4%
a 1926120
10.0%
o 1918831
10.0%
m 1646014
8.6%
g 1507668
7.9%
N 1379619
7.2%
1379619
7.2%
n 800892
 
4.2%
r 540919
 
2.8%
Other values (11) 2284463
11.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19189149
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 3039344
15.8%
s 2765660
14.4%
a 1926120
10.0%
o 1918831
10.0%
m 1646014
8.6%
g 1507668
7.9%
N 1379619
7.2%
1379619
7.2%
n 800892
 
4.2%
r 540919
 
2.8%
Other values (11) 2284463
11.9%

prev_arrival_delay_m
Real number (ℝ)

High correlation  Zeros 

Distinct100
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.85598365
Minimum0
Maximum159
Zeros1452766
Zeros (%)75.9%
Negative0
Negative (%)0.0%
Memory size14.6 MiB
2024-12-07T10:05:08.804921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum159
Range159
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.8498008
Coefficient of variation (CV)3.3292701
Kurtosis135.76144
Mean0.85598365
Median Absolute Deviation (MAD)0
Skewness8.679339
Sum1638452
Variance8.1213646
MonotonicityNot monotonic
2024-12-07T10:05:09.057859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1452766
75.9%
1 179718
 
9.4%
2 95668
 
5.0%
3 59382
 
3.1%
4 32437
 
1.7%
5 21623
 
1.1%
6 14790
 
0.8%
7 10335
 
0.5%
8 8164
 
0.4%
9 6581
 
0.3%
Other values (90) 32652
 
1.7%
ValueCountFrequency (%)
0 1452766
75.9%
1 179718
 
9.4%
2 95668
 
5.0%
3 59382
 
3.1%
4 32437
 
1.7%
5 21623
 
1.1%
6 14790
 
0.8%
7 10335
 
0.5%
8 8164
 
0.4%
9 6581
 
0.3%
ValueCountFrequency (%)
159 1
 
< 0.1%
140 1
 
< 0.1%
136 1
 
< 0.1%
134 1
 
< 0.1%
133 1
 
< 0.1%
120 1
 
< 0.1%
110 6
< 0.1%
109 2
 
< 0.1%
107 1
 
< 0.1%
106 2
 
< 0.1%

prev_departure_delay_m
Real number (ℝ)

High correlation  Zeros 

Distinct100
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.89956408
Minimum0
Maximum159
Zeros1402573
Zeros (%)73.3%
Negative0
Negative (%)0.0%
Memory size14.6 MiB
2024-12-07T10:05:09.304774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum159
Range159
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.8792037
Coefficient of variation (CV)3.2006655
Kurtosis133.35796
Mean0.89956408
Median Absolute Deviation (MAD)0
Skewness8.5940835
Sum1721870
Variance8.2898141
MonotonicityNot monotonic
2024-12-07T10:05:09.507939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1402573
73.3%
1 214085
 
11.2%
2 109271
 
5.7%
3 59559
 
3.1%
4 33058
 
1.7%
5 21813
 
1.1%
6 14820
 
0.8%
7 10493
 
0.5%
8 8284
 
0.4%
9 6601
 
0.3%
Other values (90) 33559
 
1.8%
ValueCountFrequency (%)
0 1402573
73.3%
1 214085
 
11.2%
2 109271
 
5.7%
3 59559
 
3.1%
4 33058
 
1.7%
5 21813
 
1.1%
6 14820
 
0.8%
7 10493
 
0.5%
8 8284
 
0.4%
9 6601
 
0.3%
ValueCountFrequency (%)
159 1
 
< 0.1%
137 1
 
< 0.1%
135 1
 
< 0.1%
134 2
 
< 0.1%
132 1
 
< 0.1%
120 1
 
< 0.1%
110 6
< 0.1%
109 1
 
< 0.1%
108 1
 
< 0.1%
106 2
 
< 0.1%

weighted_avg_prev_delay
Real number (ℝ)

High correlation  Zeros 

Distinct42050
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.64702311
Minimum0
Maximum114.66667
Zeros1053037
Zeros (%)55.0%
Negative0
Negative (%)0.0%
Memory size14.6 MiB
2024-12-07T10:05:09.699214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.5
95-th percentile3
Maximum114.66667
Range114.66667
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation1.9529416
Coefficient of variation (CV)3.0183491
Kurtosis155.53854
Mean0.64702311
Median Absolute Deviation (MAD)0
Skewness9.2553331
Sum1238477.3
Variance3.8139808
MonotonicityNot monotonic
2024-12-07T10:05:09.878835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1053037
55.0%
0.3333333333 13391
 
0.7%
0.6666666667 11384
 
0.6%
0.2 8948
 
0.5%
0.5 8534
 
0.4%
0.4 7818
 
0.4%
0.2857142857 6704
 
0.4%
1 5836
 
0.3%
0.25 5704
 
0.3%
0.1428571429 5366
 
0.3%
Other values (42040) 787394
41.1%
ValueCountFrequency (%)
0 1053037
55.0%
0.002898550725 1
 
< 0.1%
0.00303030303 1
 
< 0.1%
0.003361344538 4
 
< 0.1%
0.003565062389 8
 
< 0.1%
0.003787878788 22
 
< 0.1%
0.004032258065 19
 
< 0.1%
0.004301075269 35
 
< 0.1%
0.004597701149 30
 
< 0.1%
0.004926108374 38
 
< 0.1%
ValueCountFrequency (%)
114.6666667 1
< 0.1%
110.0714286 1
< 0.1%
93.76190476 1
< 0.1%
93.33333333 1
< 0.1%
80 1
< 0.1%
78.06666667 1
< 0.1%
77.19047619 1
< 0.1%
74 1
< 0.1%
72.52747253 1
< 0.1%
72.22222222 1
< 0.1%

max_station_number
Real number (ℝ)

High correlation 

Distinct51
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.983771
Minimum2
Maximum59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.6 MiB
2024-12-07T10:05:10.067897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6
Q113
median21
Q327
95-th percentile33
Maximum59
Range57
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.4428088
Coefficient of variation (CV)0.42248326
Kurtosis-0.60330558
Mean19.983771
Median Absolute Deviation (MAD)7
Skewness-0.0059528072
Sum38251256
Variance71.281021
MonotonicityNot monotonic
2024-12-07T10:05:10.322289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 145112
 
7.6%
28 124730
 
6.5%
11 91716
 
4.8%
26 90472
 
4.7%
19 89712
 
4.7%
27 88575
 
4.6%
15 81795
 
4.3%
21 69632
 
3.6%
22 69053
 
3.6%
24 65169
 
3.4%
Other values (41) 998150
52.1%
ValueCountFrequency (%)
2 7108
 
0.4%
3 11071
 
0.6%
4 22477
 
1.2%
5 29615
 
1.5%
6 40845
2.1%
7 38022
2.0%
8 44943
2.3%
9 49056
2.6%
10 59370
3.1%
11 91716
4.8%
ValueCountFrequency (%)
59 308
 
< 0.1%
54 299
 
< 0.1%
53 90
 
< 0.1%
51 80
 
< 0.1%
50 681
 
< 0.1%
49 36
 
< 0.1%
46 198
 
< 0.1%
45 160
 
< 0.1%
44 1662
 
0.1%
43 7516
0.4%

station_progress
Real number (ℝ)

High correlation 

Distinct699
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.56738401
Minimum0.026315789
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.6 MiB
2024-12-07T10:05:10.501376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.026315789
5-th percentile0.13333333
Q10.33333333
median0.57142857
Q30.8
95-th percentile1
Maximum1
Range0.97368421
Interquartile range (IQR)0.46666667

Descriptive statistics

Standard deviation0.27158674
Coefficient of variation (CV)0.47866478
Kurtosis-1.1422673
Mean0.56738401
Median Absolute Deviation (MAD)0.22857143
Skewness-0.073130352
Sum1086038.8
Variance0.073759357
MonotonicityNot monotonic
2024-12-07T10:05:10.669055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 129753
 
6.8%
0.5 68250
 
3.6%
0.6666666667 49182
 
2.6%
0.3333333333 41100
 
2.1%
0.75 35635
 
1.9%
0.8 32477
 
1.7%
0.6 31975
 
1.7%
0.4 30340
 
1.6%
0.25 26608
 
1.4%
0.2 23084
 
1.2%
Other values (689) 1445712
75.5%
ValueCountFrequency (%)
0.02631578947 101
 
< 0.1%
0.02702702703 26
 
< 0.1%
0.02941176471 1
 
< 0.1%
0.0303030303 367
 
< 0.1%
0.03125 60
 
< 0.1%
0.03225806452 4
 
< 0.1%
0.03333333333 16
 
< 0.1%
0.03448275862 61
 
< 0.1%
0.03571428571 1806
0.1%
0.03703703704 1436
0.1%
ValueCountFrequency (%)
1 129753
6.8%
0.9814814815 7
 
< 0.1%
0.98 47
 
< 0.1%
0.9782608696 5
 
< 0.1%
0.9777777778 3
 
< 0.1%
0.9772727273 31
 
< 0.1%
0.976744186 180
 
< 0.1%
0.9761904762 12
 
< 0.1%
0.9756097561 8
 
< 0.1%
0.975 12
 
< 0.1%

Interactions

2024-12-07T10:04:54.276310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:03:36.370885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:03:46.293590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:03:54.875002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:02.529967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:10.700840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:17.020456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:23.881166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:30.632087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:37.541085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:44.398568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:49.418528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:54.798355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:03:37.502184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:03:47.072195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:03:55.740172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:03.455957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:11.370367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:17.724038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:24.708190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:31.315633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:38.336004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:44.955266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:50.013629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:55.297770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:03:38.527231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:03:47.925836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:03:56.387045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:04.241681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:11.931493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:18.292588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:25.458582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:31.874076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:38.973309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:45.410771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:50.547940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:55.849349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:03:39.682073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:03:48.832687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:03:57.196039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:04.903226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:12.517919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:18.907619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:26.194906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:32.548392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:39.751581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:45.832065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:51.004605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:56.416438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:03:40.401448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:03:49.500693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:03:57.751545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:05.605535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:12.956023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:19.362703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:26.591487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:33.051243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:40.314583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:46.177788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:51.334379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:56.973492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:03:41.076464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:03:50.118776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:03:58.287441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:06.247239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:13.367658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:19.836495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:26.905720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:33.527536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:40.779992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:46.547460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:51.742954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:57.398301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:03:41.756375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:03:50.716280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:03:58.924131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:06.855773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:13.851883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:20.318127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:27.193523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:34.000566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:41.338589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:46.974317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:52.061647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:57.821645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:03:42.458743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:03:51.319622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:03:59.531382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:07.493991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:14.586227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:20.807644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:27.622963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:34.497274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:41.925177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:47.371148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:52.365522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:58.367937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:03:43.146167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:03:51.945172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:00.107103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:08.109124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:15.046670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:21.310766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:28.214373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:34.956133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:42.470969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:47.730839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:52.747596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:58.702031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:03:43.827996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:03:52.569502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:00.610069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:08.717384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:15.484798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:21.825622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:28.889504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:35.476782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:42.919249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:48.094300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:53.086037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:59.017493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:03:44.509524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:03:53.356255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:01.110947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:09.338439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:15.889931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:22.443675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:29.443962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:35.956369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:43.461742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:48.395020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:53.422306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:59.467850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:03:45.227555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:03:53.973120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:01.610574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:09.933793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:16.314263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:22.999875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:29.929447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:36.415719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:43.822169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:48.796638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-12-07T10:04:53.731324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-12-07T10:05:10.799835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
IBNRID_BaseID_Timestamparrival_delay_mlatlongmax_station_numberprev_arrival_delay_mprev_departure_delay_mstation_progressstop_numbertransformed_info_messageweighted_avg_prev_delay
IBNR1.000-0.001-0.001-0.1520.2730.5230.166-0.117-0.118-0.0380.0860.568-0.107
ID_Base-0.0011.0000.001-0.0010.0030.004-0.007-0.001-0.000-0.001-0.0040.692-0.001
ID_Timestamp-0.0010.0011.000-0.0220.0060.0040.003-0.018-0.018-0.004-0.0020.267-0.020
arrival_delay_m-0.152-0.001-0.0221.000-0.257-0.1060.1290.6100.6260.1560.2230.0130.585
lat0.2730.0030.006-0.2571.0000.243-0.004-0.230-0.244-0.014-0.0140.237-0.245
long0.5230.0040.004-0.1060.2431.0000.114-0.107-0.111-0.0220.0570.234-0.103
max_station_number0.166-0.0070.0030.129-0.0040.1141.0000.1740.150-0.1300.5530.1410.277
prev_arrival_delay_m-0.117-0.001-0.0180.610-0.230-0.1070.1741.0000.8300.1670.2700.0100.748
prev_departure_delay_m-0.118-0.000-0.0180.626-0.244-0.1110.1500.8301.0000.1480.2350.0100.667
station_progress-0.038-0.001-0.0040.156-0.014-0.022-0.1300.1670.1481.0000.6810.0190.318
stop_number0.086-0.004-0.0020.223-0.0140.0570.5530.2700.2350.6811.0000.0780.474
transformed_info_message0.5680.6920.2670.0130.2370.2340.1410.0100.0100.0190.0781.0000.010
weighted_avg_prev_delay-0.107-0.001-0.0200.585-0.245-0.1030.2770.7480.6670.3180.4740.0101.000

Missing values

2024-12-07T10:04:59.953715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-07T10:05:01.702489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

ID_BaseID_Timestampstop_numberIBNRlonglatarrival_plandeparture_planarrival_delay_mtransformed_info_messageprev_arrival_delay_mprev_departure_delay_mweighted_avg_prev_delaymax_station_numberstation_progress
0-1001326572688500578240708204128011118.013.37598852.5093792024-07-08 20:44:002024-07-08 20:45:000.0No message0.00.00.00000070.285714
1-1001326572688500578240708204138011160.09.09585148.8497922024-07-08 20:50:002024-07-08 20:50:000.0No message0.00.00.00000070.428571
2-1001326572688500578240708204148011167.013.29943752.5302762024-07-08 20:55:002024-07-08 20:56:000.0No message0.00.00.00000070.571429
3-1001326572688500578240708204158010404.013.19689852.5346482024-07-08 21:00:002024-07-08 21:03:002.0No message0.00.00.00000070.714286
4-1001326572688500578240708204168080040.013.12891752.5493962024-07-08 21:06:002024-07-08 21:07:001.0No message2.00.00.66666770.857143
5-1001326572688500578240708204178081586.013.11681052.5524802024-07-08 21:08:002024-07-08 21:09:006.0No message1.01.00.76190571.000000
6-1001326572688500578240709204128011118.013.37598852.5093792024-07-09 20:44:002024-07-09 20:45:000.0No message0.00.00.00000070.285714
7-1001326572688500578240709204138011160.08.30997054.9207832024-07-09 20:50:002024-07-09 20:50:000.0No message0.00.00.00000070.428571
8-1001326572688500578240709204148011167.013.29943752.5302762024-07-09 20:55:002024-07-09 20:56:000.0No message0.00.00.00000070.571429
9-1001326572688500578240709204158010404.013.19689852.5346482024-07-09 21:00:002024-07-09 21:03:004.0No message0.00.00.00000070.714286
ID_BaseID_Timestampstop_numberIBNRlonglatarrival_plandeparture_planarrival_delay_mtransformed_info_messageprev_arrival_delay_mprev_departure_delay_mweighted_avg_prev_delaymax_station_numberstation_progress
1914106999976718847540977240709044768005649.07.11081449.2747632024-07-09 05:01:002024-07-09 05:02:001.0No message0.00.00.061.000000
1914107999976718847540977240710044728005241.07.01878849.2304252024-07-10 04:50:002024-07-10 04:51:000.0No message0.00.00.060.333333
1914108999976718847540977240710044738005306.07.19962251.1772702024-07-10 04:50:002024-07-10 04:50:000.0No message0.00.00.060.500000
1914109999976718847540977240710044748005332.07.05708349.2440182024-07-10 04:55:002024-07-10 04:56:000.0No message0.00.00.060.666667
1914110999976718847540977240710044758005044.07.00424151.1609092024-07-10 04:56:002024-07-10 04:56:000.0No message0.00.00.060.833333
1914111999976718847540977240710044768005649.07.11081449.2747632024-07-10 05:01:002024-07-10 05:02:001.0No message0.00.00.061.000000
1914112999976718847540977240712044728005241.07.01878849.2304252024-07-12 04:50:002024-07-12 04:51:000.0No message0.00.00.060.333333
1914113999976718847540977240712044738005306.08.24372850.0707882024-07-12 04:50:002024-07-12 04:50:000.0No message0.00.00.060.500000
1914114999976718847540977240712044748005332.07.05708349.2440182024-07-12 04:55:002024-07-12 04:56:000.0No message0.00.00.060.666667
1914115999976718847540977240712044768005649.07.11081449.2747632024-07-12 05:01:002024-07-12 05:02:005.0No message0.00.00.061.000000